LMaFit

LMaFit is a MATLAB package that currently solves the following problems: Matrix Complete (MC), Sparse Matrix Separation (SMS), Matrix Compressive Sensing (MCS).

Keywords for this software

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References in zbMATH (referenced in 68 articles )

Showing results 1 to 20 of 68.
Sorted by year (citations)

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  1. Li, Qinxue; Li, Shanbin; Xu, Bugong; Liu, Yonggui: Data-driven attacks and data recovery with noise on state estimation of smart grid (2021)
  2. Shen, Yuan; Zhang, Xingying; Zhang, Xiayang: A partial PPA block-wise ADMM for multi-block linearly constrained separable convex optimization (2021)
  3. Shen, Yuan; Zuo, Yannian; Zhang, Xiayang: A faster generalized ADMM-based algorithm using a sequential updating scheme with relaxed step sizes for multiple-block linearly constrained separable convex programming (2021)
  4. Dong, Jing; Xue, Zhichao; Wang, Wenwu: Robust PCA using nonconvex rank approximation and sparse regularizer (2020)
  5. Galvan, G.; Lapucci, M.; Levato, T.; Sciandrone, M.: An alternating augmented Lagrangian method for constrained nonconvex optimization (2020)
  6. Shen, Yuan; Liu, Xin: An alternating minimization method for matrix completion problems (2020)
  7. Wei, Ke; Cai, Jian-Feng; Chan, Tony F.; Leung, Shingyu: Guarantees of Riemannian optimization for low rank matrix completion (2020)
  8. Zhao, Jianxi; Zhao, Lina: Low-rank and sparse matrices fitting algorithm for low-rank representation (2020)
  9. Ansary Karbasy, Saeid; Salahi, Maziar: A hybrid algorithm for the two-trust-region subproblem (2019)
  10. Dong, Zhengshan; Chen, Jianli; Zhu, Wenxing: Homotopy method for matrix rank minimization based on the matrix hard thresholding method (2019)
  11. Driggs, Derek; Becker, Stephen; Aravkin, Aleksandr: Adapting regularized low-rank models for parallel architectures (2019)
  12. Jiang, Bo; Lin, Tianyi; Ma, Shiqian; Zhang, Shuzhong: Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis (2019)
  13. Li, Jianwei: Unsupervised robust discriminative manifold embedding with self-expressiveness (2019)
  14. Shen, Yuan; Xu, Hongyu; Liu, Xin: An alternating minimization method for robust principal component analysis (2019)
  15. Sun, Tao; Qiao, Linbo; Li, Dongsheng: Bregman reweighted alternating minimization and its application to image deblurring (2019)
  16. Wang, Kai; Desai, Jitamitra: On the convergence rate of the augmented Lagrangian-based parallel splitting method (2019)
  17. Wang, Yu; Yin, Wotao; Zeng, Jinshan: Global convergence of ADMM in nonconvex nonsmooth optimization (2019)
  18. Yang, Xinfeng; Yan, Xiaodong; Huang, Jian: High-dimensional integrative analysis with homogeneity and sparsity recovery (2019)
  19. Zhang, Xiaoqian; Sun, Huaijiang; Liu, Zhigui; Ren, Zhenwen; Cui, Qiongjie; Li, Yanmeng: Robust low-rank kernel multi-view subspace clustering based on the Schatten (p)-norm and correntropy (2019)
  20. Balcan, Maria-Florina; Liang, Yingyu; Woodruff, David P.; Zhang, Hongyang: Matrix completion and related problems via strong duality (2018)

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